Neurology

Latest AI and machine learning research in neurology for healthcare professionals.

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Does GPT-4 have neurophobia? Localization and diagnostic accuracy of an artificial intelligence-powered chatbot in clinical vignettes.

BACKGROUND AND OBJECTIVES: This is an observational study of the performance of an artificial intell...

Machine learning and EEG can classify passive viewing of discrete categories of visual stimuli but not the observation of pain.

Previous studies have demonstrated the potential of machine learning (ML) in classifying physical pa...

As artificial intelligence goes multimodal, medical applications multiply.

Machines don't have eyes, but you wouldn't know that if you followed the progression of deep learnin...

Comparison of the carotid corrected flow time and tidal volume challenge for assessing fluid responsiveness in robot-assisted laparoscopic surgery.

We aimed to compare the ability of carotid corrected flow time assessed by ultrasound and the change...

100 Complex posterior spinal fusion cases performed with robotic instrumentation.

Robotic navigation has been shown to increase precision, accuracy, and safety during spinal reconstr...

Research on an ankle rehabilitation robot for hemiplegic patients after stroke.

This paper proposes an ankle rehabilitation robot to assist hemiplegic patients with movement traini...

Effect of exoskeleton robot-assisted training on gait function in chronic stroke survivors: a systematic review of randomised controlled trials.

OBJECTIVES: Numbers of research have reported the usage of robot-assisted gait training for walking ...

Da Vinci Meets Globus Excelsius GPS: A Totally Robotic Minimally Invasive Anterior and Posterior Lumbar Fusion.

BACKGROUND: Minimally invasive approaches to the spine via anterior and posterior approaches have be...

Improving Alzheimer Diagnoses With An Interpretable Deep Learning Framework: Including Neuropsychiatric Symptoms.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder characterized by the progressive ...

Advocating for neurodata privacy and neurotechnology regulation.

The ability to record and alter brain activity by using implantable and nonimplantable neural device...

Learning heterogeneous delays in a layer of spiking neurons for fast motion detection.

The precise timing of spikes emitted by neurons plays a crucial role in shaping the response of effe...

Deep learning, data ramping, and uncertainty estimation for detecting artifacts in large, imbalanced databases of MRI images.

Magnetic resonance imaging (MRI) is increasingly being used to delineate morphological changes under...

Using a dual-stream attention neural network to characterize mild cognitive impairment based on retinal images.

Mild cognitive impairment (MCI) is a critical transitional stage between normal cognition and dement...

Emotion recognition in EEG signals using deep learning methods: A review.

Emotions are a critical aspect of daily life and serve a crucial role in human decision-making, plan...

The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG.

Common misbehavior among children that prevents them from paying attention to tasks and interacting ...

Automatic identification of schizophrenia employing EEG records analyzed with deep learning algorithms.

Electroencephalography is a method of detecting and analyzing electrical activity in the brain. This...

The Assessment of Marcaine Versus Meperidine for Spinal Anesthesia in Anorectal Surgery: A Randomized Clinical Trial.

BACKGROUND: Spinal anesthesia (SA) for the surgical management of chronic anal fissures is favored b...

A novel framework for classification of two-class motor imagery EEG signals using logistic regression classification algorithm.

Robotics and artificial intelligence have played a significant role in developing assistive technolo...

Hybrid neuromorphic hardware with sparing 2D synapse and CMOS neuron for character recognition.

Neuromorphic computing enables efficient processing of data-intensive tasks, but requires numerous a...

CIRCE: Web-Based Platform for the Prediction of Cannabinoid Receptor Ligands Using Explainable Machine Learning.

The endocannabinoid system, which includes cannabinoid receptor 1 and 2 subtypes (CBR and CBR, respe...

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